Bibliographic record
Abstract
Couples' residential decisions are based on a large variety of factors including housing preferences, family and other social ties, socialisation and residential biography (e.g. earlier experience in the life course) and environmental factors (e.g. housing market, labour market). This study examines, firstly, to what extent people stay in, return to or leave their hometown (referred to as ‘migration type’). We refer to the hometown as the place where most of childhood and adolescence is spent. Secondly, we study which conditions shape a person’s migration type. We mainly focus on variables capturing elements of the residential biography and both partners’ family ties and family socialisation. We focus on the residential choices made at the time of family formation, i.e. when the first child is born. We employ multinomial regression modelling and cross-tabulations, based on two generations in a sample of families who mostly live in the wider Ruhr area, born around 1931 (parents) and 1957 (adult children). We find that migration type is significantly affected by a combination of both partners' place of origin, both partners' parents' places of residence, the number of previous moves, level of education and hometown population size. We conclude that complex patterns of experience made over the life course, socialisation and gendered patterns are at work. These mechanisms should be kept in mind when policymakers develop strategies to attract (return) migrants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".